Copula-based multi-event modeling and prediction using fleet service records

Akash Deep, Shiyu Zhou and Dharmaraj Veeramani

IISE Transactions, 2021, vol. 53, issue 9, 1023-1036

Abstract: Recent advances in information and communication technology are enabling availability of event sequence data from equipment fleets comprising potentially a large number of similar units. The data from a specific unit may be related to multiple types of events, such as occurrence of different types of failures, and are recorded as part of the unit’s service history. In this article, we present a novel method for modeling and prediction of such event sequences using fleet service records. The proposed method uses copula to approximate the joint distribution of time-to-event variables corresponding to each type of event. The marginal distributions of the time-to-event variables that are needed for the copula function are obtained through Cox Proportional Hazard (PH) regression models. Our method is flexible and efficient in modeling the relationships among multiple events, and overcomes limitations of traditional approaches, such as Cox PH. With simulations and a real-world case study, we demonstrate that the proposed method outperforms the base regression model in prediction accuracy of future event occurrences.

Date: 2021
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DOI: 10.1080/24725854.2020.1802792

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